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According to the IBM Institute for Business Value the market will see a rapid adoption of initial cognitive systems. The most likely candidates have moved beyond descriptive and diagnostic, predictive and routine industry-specific capabilities. 70 percent of survey respondents are currently using advanced programmatic analytics in three or more departments. In fact, the widespread adoption of cognitive systems and artificial intelligence (AI) across various industries is expected to drive worldwide revenues from nearly US$8.0 billion in 2016 to more than US$47 billion in 2020.
The analyst firm IDC predicts that the banking, retail, healthcare and discrete manufacturing industries will generate more than 50% of all worldwide cognitive/ AI revenues in 2016. Banking and retail will each deliver nearly US$1.5 billion, while healthcare and discrete manufacturing will deliver the greatest revenue growth over the 2016-2020 forecast period, with CAGRs of 69.3% and 61.4%, respectively. Education and process manufacturing will also experience significant growth over the forecast period.
Figure 1-
Credit Cognitive Scale Inc.
So what can cognitive computing really do? Three
amazing examples of this burgeoning computing model include:
·
DeepMind from Google that can
mirror some of the brain’s short-term memory properties. This computer is built
with a neural network capable of interacting with external memory. DeepMind can
“remember” using this external memory and use it to understand new information
and perform tasks beyond what it was programmed to do. The brain-like abilities
of DeepMind mean that analysts can rely on commands and information, which the
program can compare with past data queries and respond to without constant
oversight.
·
IBM Watson which has a built-in
natural language processor and hypothesis generator that it uses to perform
evaluations and accomplish dynamic learning. This system is a lot more advanced
than the digital assistants on our smartphones and allows users to ask
questions in plain language, which Watson then translates into data language
for querying.
·
The Qualcomm Zeroth Cognitive Computing Platform that relies on visual and
auditory cognitive computing in to reflect human-like thinking and actions. A
device running the platform can recognize objects, read handwriting, identify
people and understand the overall context of a setting. Zero
th’s ability to replicate intuitive experiences provides a number of opportunities within sentiment analysis. With its ability to understand scenes and context, it can decipher how people are feeling based off facial expressions or voice stress levels.
th’s ability to replicate intuitive experiences provides a number of opportunities within sentiment analysis. With its ability to understand scenes and context, it can decipher how people are feeling based off facial expressions or voice stress levels.
This shift to
cognitive computing will occur within the next 12 to 14 months for many
organizations and cognitive era success requires data centric management
culture, a common requisite for secure cloud computing. This similarity should
not be surprising because both computing models:
- Need robust and simplified data classification
processes in order to more easily deliver industry and business model
specific value;
- Require the implementation of information technology security
controls that are driven by data value and role based access control
paradigms; and
- Leverage software applications
that should be developed using ISO 27034 which is a multi-part
standard on specifying, designing/selecting and implementing information
security controls through a set of processes integrated throughout an
organization’s Systems Development Life Cycle/s (SDLC).
Companies
that are leveraging cloud today must also prepare for the cognitive computing
era. This blend of cloud and cognitive has, in fact, created a brand new application
development model.
Referred
to as “Cognitive on cloud”, this model delivers cognitive services
running in the cloud that are consumable via representational state transfer
(REST) APIs. These services are available as part of platform-as-a-service
(PaaS) offerings such as Bluemix and can be easily bound to an application
while coding.
Using
this approach, cognitive analytics such as voice (tone analyzer,
speech-to-text) and video (face detection, visual recognition) capabilities
enables quick analysis of petabytes of unstructured data. Developing cognitive
applications to run on mobile devices has provided new insights which help organizations
create totally new revenue streams. When selecting a cloud service provider
however cognitive on cloud ROI requires more than just a total cost of
ownership comparison. In addition to this basic analysis, an organization must
consider which cloud is cognitive enabled at the Platform-as-a-Service (PaaS)
layer. The convergence of cognitive computing and cloud is driving this cognitive-oriented
digital economy and the potential return is seemingly unlimited.
This post was brought to you by IBM Global Technology Services. For more content like this, visit IT Biz Advisor.
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